Within-patient algorithm to predict heart failure decompensation

ABSTRACT

This document discusses, among other things, systems and methods for predicting heart failure decompensation using within-patient diagnostics. A method comprises detecting an alert status of each of one or more sensors; calculating an alert score by combining the detected alerts; and calculating a composite alert score, the composite alert score being indicative of a physiological condition and comprising a combination of two or more alert scores.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/616,450, filed on Dec. 27, 2006, now issued as U.S. Pat. No.7,629,889, the specification of which is hereby incorporated byreference.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright 2006, Cardiac Pacemakers, Inc. All Rights Reserved.

TECHNICAL FIELD

This patent document pertains generally to implantable medical devices,and more particularly, but not by way of limitation, to systems andmethods for predicting heart failure decompensation using within-patientdiagnostics.

BACKGROUND

Implantable medical devices (IMDs), including cardiac rhythm managementdevices such as pacemakers and implantable cardioverter/defibrillators,typically have the capability to communicate with an external device,such as an external programmer, via wireless telemetry, such as aradio-frequency (RF) or other telemetry link. While an externalprogrammer is typically provided to program and modify the operatingparameters of an IMD, modem IMDs also include the capability forbidirectional communication so that information, such as physiologicaldata, can be transmitted to the programmer. Home health care remotemonitoring systems can also communicate with the IMD and collect thepatient and patient-related data. In addition, some monitoring systemscan also collect other objective or subjective data using additionalexternal sensors, such as a blood pressure cuff, a weight scale, or aspecialized device that prompts the patient with questions regardingtheir health state. Some home health care monitoring systems cancommunicate with a centralized system, such as directly or using anetworked system. Centralized systems, including medical practicesystems, provide an efficient mode for physicians and other medicalpractitioners to manage patient-related data.

Overview

Example 1 describes a method comprising: detecting an alert status ofeach of one or more sensors; calculating an alert score by combining thedetected alerts; and calculating a composite alert score, the compositealert score being indicative of a physiological condition and comprisinga combination of two or more alert scores.

In Example 2, the method of Example 1 is optionally performed such thatcalculating the alert score includes combining detected alerts occurringover time.

In Example 3, the methods of any one or more of Examples 1 or 2 areoptionally performed such that detecting the alert status includesdetecting a discrete value or a binary value.

In Example 4, the methods of any one or more of Examples 1-3 areoptionally performed such that the discrete value is indicative of oneof two or more states.

In Example 5, the methods of any one or more of Examples 1-4 areoptionally performed such that the binary value is indicative of a heartfailure decompensation condition or a non-heart failure decompensationcondition.

In Example 6, the methods of any one or more of Examples 1-5 areoptionally performed such that the binary value is indicative of ahigher likelihood of death in a particular timeframe or a lowerlikelihood of death in the particular timeframe.

In Example 7, the methods of any one or more of Examples 1-6 areoptionally performed such that the binary value is indicative of ahigher likelihood of a change in quality of life in a particulartimeframe or a lower likelihood of a change in quality of life in theparticular timeframe.

In Example 8, the methods of any one or more of Examples 1-7 areoptionally performed such that detecting the alert status includes usinga threshold value.

In Example 9, the methods of any one or more of Examples 1-8 areoptionally performed such that the threshold value includes one of arelative change from a baseline value, an absolute value, or a specifieddeviation from a baseline value.

In Example 10, the methods of any one or more of Examples 1-9 areoptionally performed such that calculating the alert score includescalculating a weighted function of two or more detected alert statuses.

In Example 11, the methods of any one or more of Examples 1-10 areoptionally performed such that calculating the weighted functionincludes using one or more weights, wherein the weights are one of:equal, unequal, or adaptive.

In Example 12, the methods of any one or more of Examples 1-11 areoptionally performed such that calculating the weighted functionincludes using one or more weights that are related to one or more of:time, a number or type of the one or more sensors, a patient population,or one or more characteristics of a current patient.

In Example 13, the methods of any one or more of Examples 1-12 areoptionally performed such that the composite alert score indicates alikelihood of heart failure decompensation.

In Example 14, the methods of any one or more of Examples 1-13 areoptionally performed such that the composite alert score indicates alikelihood of death in a timeframe.

In Example 15, the methods of any one or more of Examples 1-14 areoptionally performed such that the composite alert score indicates alikelihood of a change in quality of life in a timeframe.

In Example 16, the methods of any one or more of Examples 1-15 areoptionally performed such that calculating the composite alert scoreincludes using a weighted function.

In Example 17, the methods of any one or more of Examples 1-16 areoptionally performed comprising: comparing the composite alert score toa composite alert score threshold; and providing an indication of ahigher likelihood of a physiological condition when the composite alertscore exceeds the composite alert score threshold.

In Example 18, the methods of any one or more of Examples 1-17 areoptionally performed comprising: choosing an initial value for thecomposite alert score threshold; and dynamically adjusting the compositealert score threshold to improve one or more performance measuresrelated to false positives or false negatives for a particular patient.

In Example 19, the methods of any one or more of Examples 1-18 areoptionally performed such that choosing the initial value includes usinga value determined during a learning period.

In Example 20, the methods of any one or more of Examples 1-19 areoptionally performed such that adjusting the composite alert score isperformed automatically.

In Example 21, the methods of any one or more of Examples 1-20 areoptionally performed such that the initial value is set to anartificially high or low value.

In Example 22, the methods of any one or more of Examples 1-21 areoptionally performed such that the composite alert score threshold isdynamically adjusted.

Example 23 describes a system comprising a patient device comprising: acommunication module adapted to detect an alert status of each of one ormore sensors; an analysis module adapted to: calculate an alert score bycombining the detected alerts; and calculate a composite alert score,the composite alert score being indicative of a physiological conditionand comprising a combination of two or more alert scores.

In Example 24, the system of Example 23 is optionally configured suchthat calculating the alert score includes combining detected alertsoccurring over time.

In Example 25, the system of any one or more of Examples 23 or 24 areoptionally configured comprising a sensor adapted to output a binaryvalue indicative of a heart failure decompensation condition or anon-heart failure decompensation condition.

In Example 26, the system of any one or more of Examples 23-25 areoptionally configured such that the sensor is adapted to set the alertstatus using a threshold value.

In Example 27, the system of any one or more of Examples 23-26 areoptionally configured such that the threshold value includes one of arelative change from a baseline value, an absolute value, or a specifieddeviation from a baseline value.

In Example 28, the system of any one or more of Examples 23-27 areoptionally configured such that the analysis module is adapted tocalculate the alert score using a weighted function of two or moredetected alert statuses.

In Example 29, the system of any one or more of Examples 23-28 areoptionally configured such that the composite alert score indicates alikelihood of heart failure decompensation.

In Example 30, the system of any one or more of Examples 23-29 areoptionally configured such that the composite alert score indicates alikelihood of death in a timeframe.

In Example 31, the system of any one or more of Examples 23-30 areoptionally configured such that the composite alert score indicates alikelihood of a change in quality of life in a timeframe.

In Example 32, the system of any one or more of Examples 23-31 areoptionally configured such that the analysis module is adapted to:compare the composite alert score to a composite alert score threshold;and provide an indication of a higher likelihood of a physiologicalcondition when the composite alert score exceeds the composite alertscore threshold.

Example 33 describes an apparatus comprising: means for detecting analert status of each of one or more sensors; means for calculating analert score by combining the detected alerts; and means for calculatinga composite alert score, the composite alert score being indicative of aphysiological condition and comprising a combination of two or morealert scores.

This overview is intended to provide an overview of the subject matterof the present patent application. It is not intended to provide anexclusive or exhaustive explanation of the invention. The detaileddescription is included to provide further information about the subjectmatter of the present patent application.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsdescribe substantially similar components throughout the several views.Like numerals having different letter suffixes represent differentinstances of substantially similar components. The drawings illustrategenerally, by way of example, but not by way of limitation, variousembodiments discussed in the present document.

FIG. 1 illustrates portions of a system that enables physician-patientcommunication.

FIG. 2 is a detailed schematic view illustrating portions of a systemthat measures and detects variance in patient-related data to identifyacute changes that may indicate an onset of a physiological condition.

FIG. 3 illustrates a method of using a composite alert score to detectan increased likelihood of a disease state or onset of a physiologicalcondition.

FIGS. 4-6 are diagrams illustrating examples of relationships betweenalert values, alert scores, and composite alert scores.

FIG. 7 illustrates an example of a method of using sensed patientactions to determine a level of patient compliance.

FIG. 8 illustrates an example of a method of determining a complianceindex over two or more different patient responses.

FIGS. 9A-9F are charts illustrating examples of recorded patient actionsin response to at least one specific request.

FIG. 10 illustrates an example of a method of deriving a probabilisticindex based on a particular patient compared to a patient population.

FIGS. 11A-C illustrate examples of a physical activity cumulativedistribution function (CDF) chart, an SDANN CDF chart, and a Footprint %CDF chart.

FIG. 12 is an example of a probability distribution function chart thatillustrates reference group patients' physical activity levels.

FIGS. 13 and 14 are diagrams illustrating examples of control and dataflow between patient analysis processes.

FIG. 15 illustrates a cross-feedback configuration of patient analysisprocesses.

FIG. 16 is a dataflow diagram illustrating an example of a physicianfeedback process.

FIG. 17 illustrates an example of a feedback loop between a centralsystem and a physician.

FIG. 18 is a flowchart illustrating an example of a method of usingphysician feedback to modify the execution of patient analysis routines.

FIG. 19 is an example of a user-interface to allow a medicalprofessional to submit input or feedback to a control system.

FIG. 20 is a control flow diagram illustrating an example of aninteraction between a user-interface system and a control system inaccordance with the user-interface illustrated in FIG. 19.

FIG. 21 is an example of a user-interface to allow a medicalprofessional to submit input or feedback to a control system.

FIG. 22 is a control flow diagram illustrating an example of aninteraction between a user-interface system and a control system inaccordance with the user-interface illustrated in FIG. 21.

FIG. 23 is another example of a user-interface to allow a medicalprofessional to submit feedback to a control system.

FIG. 24 is a control flow diagram illustrating an example of aninteraction between a user-interface system and a control system inaccordance with the user-interface illustrated in FIG. 23.

FIG. 25 is another example of a user-interface.

FIG. 26 is a control flow diagram illustrating an example of aninteraction between a user-interface system and a control system inaccordance with the user-interface illustrated in FIG. 25.

FIG. 27 is another example of a user-interface to control one or moresensors.

FIG. 28 is a control flow diagram illustrating an example of aninteraction between a user-interface system and a control system inaccordance with the user-interface illustrated in FIG. 27.

DETAILED DESCRIPTION

The following detailed description includes references to theaccompanying drawings, which form a part of the detailed description.The drawings show, by way of illustration, specific embodiments in whichthe invention may be practiced. These embodiments, which are alsoreferred to herein as “examples,” are described in enough detail toenable those skilled in the art to practice the invention. Theembodiments may be combined, other embodiments may be utilized, orstructural, logical and electrical changes may be made without departingfrom the scope of the present invention. The following detaileddescription is, therefore, not to be taken in a limiting sense, and thescope of the present invention is defined by the appended claims andtheir equivalents.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one. In this document, the term“or” is used to refer to a nonexclusive or, unless otherwise indicated.Furthermore, all publications, patents, and patent documents referred toin this document are incorporated by reference herein in their entirety,as though individually incorporated by reference. In the event ofinconsistent usages between this document and those documents soincorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

Overview

FIG. 1 illustrates portions of a system that enables physician-patientcommunication. In the example of FIG. 1, a patient 100 is provided withan implantable medical device (IMD) 102. Examples of implantable medicaldevices include a pacemaker, an implantable cardioverter defibrillator(ICD), a cardiac resynchronization therapy pacemaker (CRT-P), a cardiacresynchronization therapy defibrillator (CRT-D), a neurostimulationdevice, a deep brain stimulation device, a cochlear implant or a retinalimplant. In some examples, the IMD 102 is capable of sensingphysiological data and storing such data for later communication.Examples of physiological data include implantable electrograms, surfaceelectrocardiograms, heart rate intervals (e.g., AA, VV, AV or VAintervals), electrogram templates such as for tachyarrhythmiadiscrimination, pressure (e.g., intracardiac or systemic pressure),oxygen saturation, activity, heart rate variability, heart sounds,impedance, respiration, intrinsic depolarization amplitude, or the like.

The IMD 102 is capable of bidirectional communication 103 with anexternal transceiver 104. In various examples, the IMD 102 receivescommands from the transceiver 104 and may also communicate one or morepatient indications to the transceiver 104. Examples of patientindications may include such things as heart rate, heart ratevariability, data related to tachyarrhythmia episodes, hemodynamicstability, activity, therapy history, autonomic balance motor trends,electrogram templates for tachy discrimination, heart rate variabilitytrends or templates, or trends, templates, or abstractions derived fromsensed physiological data. In some examples, patient indications includeone or more physiological indications, such as the physiological datadescribed above. In another example, the IMD 102 may also communicateone or more device indications to the transceiver 104. Examples ofdevice indications include lead/shock impedance, pacing amplitudes,pacing thresholds, or other device metrics. In certain examples, the IMD102 may communicate sensed physiological signal data to the transceiver104, which may then communicate the signal data to a remote device, suchas for processing.

Typically, the transceiver 104 is located in close proximity to thepatient 100. The transceiver 104 may be included within or attached to apersonal computer or a specialized device, such as a medical deviceprogrammer. In one example, the transceiver 104 is a hand-held devicethat is capable of connecting to a local computer 106. Typically, aconnection 105 can be made using a hard-wired connection (e.g., serial,USB, Firewire) or a wireless connection (e.g., RF, IR). In someexamples, the local computer 106 is a specialized device or a personalcomputer. In certain examples, the local computer 106 is adapted tocommunicate with a remote server system 108. The communication linkbetween the local computer 106 and the remote server system 108 istypically made through a computer or telecommunications network 110. Thenetwork 110 may include, in various examples, one or more wired orwireless networking such as the Internet, satellite telemetry, cellulartelemetry, microwave telemetry, or other long-range communicationnetworks.

In an example, one or more external sensors 107 are adapted tocommunicate with the transceiver 104 and may transmit and receiveinformation, such as sensed data. External sensors 107 may be used tomeasure patient physiological data, such as temperature (e.g., athermometer), blood pressure (e.g., a sphygmomanometer), bloodcharacteristics (e.g., glucose level), body weight, physical strength,mental acuity, diet, or heart characteristics. An external sensor 107may also include one or more environmental sensors. The external sensors107 can be placed in a variety of geographic locations (in closeproximity to patient or distributed throughout a population) and canrecord non-patient specific characteristics such as, for example,temperature, air quality, humidity, carbon monoxide level, oxygen level,barometric pressure, light intensity, and sound.

External sensors 107 can also include devices that measure subjectivedata from the patient. Subjective data includes information related to apatient's feelings, perceptions, and/or opinions, as opposed toobjective physiological data. For example, the “subjective” devices canmeasure patient responses to inquiries such as “How do you feel?”, “Howis your pain?” and “Does this taste good?” Such a device may also beadapted to present interrogatory questions related to observationaldata, such as “What color is the sky?” or “Is it sunny outside?” Thedevice can prompt the patient and record responsive data from thepatient using visual and/or audible cues. For example, the patient canpress coded response buttons or type an appropriate response on akeypad. Alternatively, responsive data may be collected by allowing thepatient to speak into a microphone and using speech recognition softwareto process the response.

In some examples, the remote server system 108 comprises one or morecomputers, such as a database server 114, a network server 116, a fileserver 118, an application server 120 and a web server 122. In certainexamples, one or more terminals 112A, 112B, . . . , 112N are locally orremotely connected to the remote server system 108 via network 110. Theterminals 112 are communicatively coupled to the remote server system108 using a wired 124 or a wireless connection 126. Examples ofterminals 112 may include personal computers, dedicated terminalconsoles, handheld devices (e.g., a personal digital assistant (PDA) orcellular telephone), or other specialized devices. In various examples,one or more users may use a terminal 112 to access the remote serversystem 108. For example, a customer service professional may use aterminal 112 to access records stored in the remote server system 108 toupdate patient records. As another example, a physician or clinician mayuse a terminal 112 to receive or provide patient-related data, such ascomments regarding a patient visit, physiological data from a test orcollected by a sensor or monitor, therapy history (e.g., IMD shock orpacing therapy), or other physician observations.

In some examples, the IMD 102 is adapted to store patient data and touse the data to provide tailored therapy. For example, using historicalphysiological data, an IMD 102 may be able to discriminate betweenlethal and non-lethal heart rhythms and deliver an appropriate therapy.However, it is often desirable to establish a proper baseline ofhistorical data by collecting a sufficient amount of data in the IMD102. In some examples, a “learning period” of some time (e.g., thirtydays) is used to establish the baseline for one or more physiologicalsignals. An IMD 102 may, in an example, store a moving window of data ofoperation, such as a time period equal to the learning period, and mayuse the information as a baseline indication of the patient's biorhythmsor biological events.

Once the baseline is established, then acute and long-term patientconditions may be determined probabilistically. The baseline may beestablished by using historical patient records or by comparing apatient to a population of patients. In an example, a diagnostictechnique uses a patient-based baseline to detect a change in apatient's condition over time. Examples of a diagnostic technique thatuses a patient-derived baseline are described in the next section.

In an example, patient diagnostics are automatically collected andstored by the implanted device 102. These values may be based on thepatient's heart rate or physical activity over a time period (e.g.,24-hour period) and each diagnostic parameter is saved as a function ofthe time period. In one example, heart-rate based diagnostics utilizeonly normal intrinsic beats. For heart rate variability (HRV) patientdiagnostics, the average heart rate can be found at each interval withinthe time period, for example, at each of the 288 five-minute intervalsoccurring during 24 hours. From these interval values, the minimum heartrate (MinHR), average heart rate (AvgHR), maximum heart rate (MaxHR) andstandard deviation of average normal-to-normal (SDANN) values may becalculated and stored. In one example, the implanted device 102 computesa HRV Footprint® patient diagnostic that can include a 2-dimensionalhistogram that counts the number of daily heartbeats occurring at eachcombination of heart rate (interval between consecutive beats) andbeat-to-beat variability (absolute difference between consecutiveintervals). Each histogram bin contains the daily total for thatcombination. The percentage of histogram bins containing one or morecounts can be saved each day as the footprint percent (Footprint %). Theimplanted device 102 can also provide an Activity Log® patientdiagnostic (Activity %), which can include a general measure of patientactivity and can be reported as the percentage of each time periodduring which the device-based accelerometer signal is above a thresholdvalue.

Within-Patient Diagnosis

In certain examples, a within-patient diagnostic technique measuresshort-term variance of one or more patient-related physiologicalparameters to detect acute changes in physiologic sensor values. Themeasured physiological parameters may be compared to a baseline value todetect changes that exceed a threshold value. These changes may occurwithin a short period before a patient experiences an onset of aphysiological condition and as such, an alert may be generated whenchanges exceed the threshold amount.

FIG. 2 is a detailed schematic view illustrating portions of a system200 that measures and detects variance in patient-related data toidentify acute changes that may indicate an onset of a physiologicalcondition. In the system 200, two or more detectors 202A, 202B, . . . ,202N are connected to one or more sensors 204. Sensors 204 may includeimplanted or external sensors, such as those described above. Sensors204 may be configured to automatically collect patient-related data(e.g., a heart rate monitor) or be configured to operate by usercommands (e.g., an interrogatory device with a display, or a weightscale). The patient-related data may include sensed physiological data,sensed environmental data, or data collected from a patient in responseto a query or request. Examples of the sensors 204 include, withoutlimitation, an electrocardiogram, an accelerometer, a pressure sensor, acardiac output (CO) detector, a heart rate monitor, an interrogatorydevice, a weight scale, and a microphone. Examples of sensed valueinclude, without limitation, standard deviation of averagednormal-to-normal (SDANN) cardiac depolarization interval trends, heartrate minimum (HRMin), physical activity, or a patient compliance index(as described below). Each detector 202 may include hardware or softwareto evaluate the one or more input signals from the one or more sensors204, such as to determine a value of an alert status associated with thesensor-detector pair.

Detectors 202 may be configured to provide an alert status when one ormore conditions are detected. In an example, the alert status is basedon comparing one or more parameters (e.g., sensed values) to one or morethreshold values, such as to determine whether the one or moreparameters exceeds or falls below its corresponding threshold value.Threshold values may be configured as an absolute value (e.g., a minimumor maximum acceptable safety value) or based on a difference or changefrom a baseline or other known value. For example, a threshold may beconfigured as a maximum (or minimum) percent change from a value (e.g.,baseline value); as a standard deviation value from a value; or anabsolute change from a value (e.g., an increase of five points). In anexample, the maximum percent change threshold value is computed by usinga baseline value, such that if the sensed value (or daily average ofsensed values) exceeds the percent change threshold from the baselinevalue an alert status is found. Baseline values may be calculated usinga central tendency (e.g., average, mean, median, mode, etc.) or othercomposite of two or more sensed values over a particular time period(e.g., day, week, month, training period, etc.). An initial thresholdvalue may be determined using performance of the within-patientdiagnostic technique during a training or learning period (e.g., thefirst 30 days of operation of a new device). One or more thresholdvalues may be adjusted, automatically or manually, from the initialthreshold value during later performance.

In some examples, an alert status is reflective of whether an eventoccurred. For example, if a patient is requested to perform an action(e.g., take medicine or exercise daily) and fails to perform therequested action, then an alert may be generated. In various examples,the alert status may be represented as a binary value, a substantiallycontinuous value, or a discrete value. Binary values may represent, forexample, whether a patient action was detected (e.g., yes/no) or whethera two-state condition exists (e.g., on/off, hot/cold). Additionally,binary values may indicate whether a patient is more or less likely toexperience a health change, such as a change to quality of life, anonset of a disease state (e.g., heart failure decompensation), or death.Discrete values may indicate, for example, a multi-state condition(e.g., low/medium/high) or a scaled value, such as a subjective ratingof pain on a scale of one to five. Substantially continuous values mayindicate, for example, a normalized scale, such as a scale of zero toone, however, such values may be quantized by an analog-to-digitalconverter.

Each alert status is communicated to a fusion machine 208 using acorresponding data pathway 206A, 206B, . . . , 206N. Depending on theconfiguration of the detectors 202 and the fusion machine 208, one ormore of the corresponding data pathways 206 may be wired or wireless.For example, in certain examples, the detectors 202 and the fusionmachine 208 are integrated into an IMD. In other examples, one or moredetectors 202 may be located separate from the IMD and possibly separatefrom each other. In this case, the fusion machine 208 may be integratedinto one or more detectors 202 or it may comprise a separate machine.

Moreover, although the example illustrated in FIG. 2 depicts a detector202 associated with an alert status value (communicated over a datapathway 206), sensors 204, detectors 202, and data pathways 206 may becombined or multiplexed in various ways. For example, a detector 202 mayuse one or more sensors 204 to determine an alert status value. Asanother example, two or more detectors 202 may be used in combination todetermine a particular alert status value. In another example, sensors204 or detectors 202 may be reused in multiple combinations orpermutations with other sensors 204 or detectors 202 to derive alertstatus values. Such combinations or permutations of sensors 204 ordetectors 202 may be advantageous to provide an alert status value thatreflects a more complex decision or determination.

The two or more detectors 202 may communicate their alert status valuesto a first fusion module 210. The first fusion module 210 calculates analert score using the alert status from one or more detectors 202. In anexample, the first fusion module 210 uses a weighted function tocalculate the alert score. The weights in the weighted function may beadapted for a particular patient or a particular population of patients,such as by adjusting the weights based on prior knowledge of thesuspected patient condition and the types or numbers of sensors used.For example, patients at high risk of heart failure decompensation mayexhibit an unusually low physical activity or heart rate variability(HRV). By increasing the sensitivity of these sensors (e.g., decreasinga threshold value), a lower physical activity value or a lower HRV valuemay be detected earlier.

In another example, weights in the weighted function may be based ontime, the number or types of sensors, or a confidence value associatedwith a sensor 204 or detector 202. For example, more recent alert valuesmay be weighed more than less recent alert values; a particular type ofsensor may be considered more reliable and assigned a comparativelyhigher weight than sensors considered less reliable. As another example,in a situation where more than one sensor is used to determine an alertvalue, the number of sensors used to determine such an alert status maybe used to assign a weight, such that alert values calculated using moresensors may be considered more reliable and thus, have a higher weightcompared to alert values calculated using fewer sensors. In yet anotherexample, weights may be assigned using a cost function. For example,individual decisions could be weighted according to their reliability,such that the weights may be regarded as a function of the probabilityof miss or the probability of false alarm of an individual detection.

In addition, weights may be modified, such as between alert scorecalculations, to adaptively adjust to changing conditions. The alertscore may be calculated periodically or recurrently, such as hourly,daily, or weekly. In an example, after calculating the alert score, thefirst fusion module 210 stores the alert score in an alert score memory212. The history of alert scores may be used to track changes or infurther processing, as described below. The alert score memory 212 mayinclude a database, files, random access memory, or other storage unit.

The alert score may be communicated from the first fusion module 210 tothe second fusion module 214. In another example, the second fusionmodule 214 accesses a storage unit, such as the alert score database212, to obtain the current alert score. The second fusion module 214also accesses the same or a different storage unit to obtain one or morehistorical alert scores. In an example, a fixed number of historicalalert scores are obtained from the storage unit, such as to obtain a“moving window” of the most recent historical alert score data. Forexample, when alert scores are calculated and stored weekly, then threeprior calculated alert scores may be obtained, along with the currentalert score, to effectively view a month's worth of alert score data.The second fusion module 214 uses the one or more historical alertscores in combination with the current alert score to calculate acombined alert score (CAS). In an example, the CAS is a weightedfunction of the alert scores. In various examples, weights in theweighted function may be equal, unequal, adaptive based on one or morepatient characteristics, or based on time (e.g., more recent alertscores are given a higher weight, being considered more relevant, whileless recent alert scores are given a lower weight).

In an example, the second fusion module 214 communicates the CAS to acomparator module 216. The comparator module 216 compares the CAS to athreshold CAS value. In various examples, the threshold CAS value is anabsolute value, or may be based on a percent change from a baseline orother standard value. In other examples, the threshold CAS value isdynamic or static. For example, the threshold CAS value may be manuallyset by a user. The user may change the value at recurrent or periodicintervals. For example, a user may set the CAS threshold to somearbitrary high value and then dynamically or manually adjust the CASthreshold, such as to fine tune false positive or false negative rates(e.g., specificity or sensitivity).

Sensitivity generally refers to the ability of the detection scheme toeffectively detect a particular result. Sensitivity can be expressedwith the formula: sensitivity=(true positives)/(true positives+falsenegatives). Thus, a higher sensitivity generally indicates that ananalysis correctly characterizes more true positives or eliminates falsenegatives.

Specificity generally refers to the ability of the detection scheme toavoid improper classifications. Specificity can be expressed with thefunction: specificity=(true negatives)/(true negatives+false positives).Thus, a higher specificity generally reflects more accurateclassification of true negatives or reduction of false positives.

In other examples, the threshold CAS value is determined automatically.In an example, the threshold updater module 224 uses one or more inputparameters to configure or update the threshold CAS value. Inputparameters may include things such as the time, the number of sensors ordetectors, one or more patient characteristics, a physician's orclinician's preference, the previous threshold CAS value, or the CAS.The threshold updater module 224 may communicate the current thresholdvalue to the comparator module 216 for use in the comparison. In certainexamples, the threshold CAS value is established using a constant falsealarm rate (CFAR) technique, such as described in Siejko et al U.S.patent application Ser. No. 11/276,735, entitled PHYSIOLOGICAL EVENTDETECTION SYSTEMS AND METHODS, filed on Mar. 13, 2006, which is assignedto the assignee of the present patent application, and which isincorporated herein by reference in its entirety, including itsdescription of CFAR event detection techniques.

When the CAS exceeds the threshold CAS value, then the comparator module216 provides an indication of this state to the alert module 220. Thealert module 220 may, in some examples, record one or more aspects ofthe generated alert, such as in alert history database 222. The alertmodule 220 may communicate the alert state to a communication module226, for communication to a user (e.g., a physician or clinician).

While FIG. 2 illustrates an example of a system 200 that implementsfusion logic in a fusion machine 208, portions of the processing mayoccur at the sensor 204, detector 202, or be distributed among severalprocessing machines. For example, a sensor 204 or detector 202 mayinclude memory to record one or more sensed values over time and mayonly transmit a central tendency (e.g., mean, median, or mode) to thefirst fusion module 210 for further processing. As a further example,the fusion machine 208 may be located at a central server, programmer,or patient device.

FIG. 3 illustrates a method 300 of using a composite alert score todetect an increased likelihood of a disease state or onset of aphysiological condition. At 302, one or more alert status values aredetected. Alert status values may be binary (e.g., on/off, yes/no,high/low), substantially continuous (e.g., 1.4, 2.9, 9.34) or discrete(e.g., 1 out of 5, 2 out of 4). At 304, an alert score is calculatedusing the alert status values. In an example, the alert score is aweighted function, such that:Alert Score (AS)=Alert₁ *w ₁+Alert₂ *w ₂+ . . . +Alert_(m) w _(m)

where weights w₁, w₂, . . . , w_(m) may be modified to weigh one alertvalue higher or lower than another alert value based on a factor, suchas a patient characteristic or a sensor confidence level. In an example,alerts may be temporally related. For example, an alert status may bedetected on a periodic or recurrent basis, such as daily, from aparticular sensor. In another example, alerts may be otherwiseassociated. For example, alert statuses may be detected from one or moreof similar types of sensors (e.g., implanted and external heart ratemonitors), such that if an alert is detected from one sensor, then thealert may be considered to be active for all related or associatedsensors. In another example, all related or associated sensors arepolled and an alert is detected when some plurality or all concur on analert state.

At 306, two or more alert scores are combined into a composite alertscore (CAS). In an example, the CAS is a weighted function of alertscores, such that:Composite Alert Score (CAS)=AS_(i) *w _(i)+AS_(i-1) *w _(i-1)+ . . .+AS_(n) *w _(n)

where weights w_(i), w_(i-1), . . . , w_(n) may be modified to weigh onealert score higher or lower than another alert score based on a factor,such as time, patient changes over time, or the like. In an example,AS_(i) is the alert score of the current period and AS_(i-1) is thealert score for the previous period, etc. Periods may be days, weeks,months, or some other regular time interval. At 308, the CAS is comparedto a threshold value. In an example, the threshold is fixed, however itmay be adapted, such as for particular patients or over time in otherexamples. When the CAS is over the threshold value in this example, thenat 310, an alert state is set. If the CAS does not exceed the thresholdvalue in this example, then at 312, the alert state is not set. Invarious examples, the alert state may indicate one or more of an onsetof a physiological condition, a change in a physiological condition, ora predictive measure of a possibility of an onset of such aphysiological condition. For example, the alert state may be used toassist in predicting physiological or patient-related events, such as HFdecompensation, lead fracture, sudden cardiac death (SCD), or myocardialinfarction (MI). Additionally, the alert state may be indicative of orused for determining a likelihood of a change in a patient's quality oflife or a likelihood of patient death in a particular time period ortime frame. Portions or all of the method 300 may be executed ondifferent processing machines, for example, method 300 could be executedby a central server, a programmer, or a patient device.

FIG. 4 is a diagram illustrating an example of relationships betweenalert values, alert scores, and composite alert scores. In an example,alert values 400 are sensed or detected over time and associated with aparticular sensor 402. Alert values 400 may be combined first withrespect to a particular sensor 402, for example, AS_(i), AS_(j), . . . ,AS_(z) 404. The alert scores combined with respect to each sensor maythen be combined to form the composite alert score, CAS 406.Alternatively, alert values 400 may be combined first with respect to aparticular time slice, such that AS₁, AS₂, . . . , AS_(N) 408.Similarly, the alert scores combined with respect to each particulartime slice may then be combined into a composite alert score 406. Asdescribed above, the calculation of the alert scores, either withrespect to a particular sensor or with respect to a particular timeslice, may include the use of a weighted function. In addition, thecalculation of the combined alert score 406 may include a weightedfunction.

In other examples, as illustrated in FIGS. 5-6, alert scores may becalculated using various combinations of alert values. FIG. 5 is adiagram illustrating relationships between alert values, alert scores,and composite alert scores. In FIG. 5, alert values 500 are used invarious combinations to determine alert scores 502A, 502B, 502C. Forexample, alert score AS 502A is composed of alert values A_(i) andA_(j), alert score AS′ 502B is composed of alert values A_(y) and A_(z),and alert score AS″ 502C is composed of alert values A_(j) and A_(y).Alert scores 502A, 502B, 502C may be combined to form a composite alertscore 504. Alert values 500 may be obtained from the same sensor overtime or from two or more sensors. In an example, when alert values 500are obtained from the same sensor, the alert values 500 may bedetermined at periodic or recurring time intervals, such as daily,hourly, or the like. In another example, when alert values 500 areobtained from two or more sensors, the values 500 may be obtained atapproximately the same time.

FIG. 6 illustrates another relationship between alert values, alertscores, and composite alert scores. Provided an array or matrix of alertvalues 600, various subsets of alert values 600 may be combined to formalert scores, such as AS₁ 602 and AS₂ 604. Alert scores 602, 604 may becombined to form a composite alert score 606. As described above withreference to other examples, relationships illustrated in FIGS. 5 and 6may include weighted functions.

Surrogate Measure of Patient Compliance

Measurements of patient compliance may provide a general indication ofhow closely a patient follows a physician's or clinician's direction orinstruction. Patients who are non-compliant in one or more ways, such asconcerning diet, exercise, or medicine, may also be non-compliant withregard to other medical advice or instruction. Non-compliant patientsmay benefit from closer observation or follow-up by their physician orclinician. The observation or follow-up may assist the physician orclinician in managing an increased medical risk due to non-complianceand increasing the patient's overall compliance. In addition,non-compliant patients may benefit from re-evaluating, modifying,ceasing, or implementing new therapies.

In some examples, patient compliance may be measured by detectingwhether one or more requested actions were performed by the patient.Performance may be analyzed using one or more indexes, such as withrespect to frequency, time, or technique or the like. For example, apatient who is requested to weigh himself unclothed daily at 9:00 AM mayhave a high frequency compliance score if he consistently weighs himselfevery day. However, if the weigh-ins are sporadically timed, for examplefrom 8:30 AM to 11:00 AM, then the patient may be associated with arelatively lower time compliance score. In addition, if the patient'sweight measured during weigh-ins differs by more than a few pounds,which may be considered normal daily weight variance, then it may bededuced that the patient was clothed during some weigh-ins and thus, maybe associated with a relatively lower technique compliance score.

Thus, frequency compliance may be measured by a frequency complianceindex score, and can be conceptualized as how often the requested actionis documented. In an example, the frequency compliance score is measuredas a ratio of missed measurements over a particular time period. In sucha configuration, a higher frequency compliance score may indicate alower patient compliance. In another example, an inverse ratio is used,that is, the number of successful measurements over a particular timeperiod, where a higher compliance score may indicate a more patientcompliance.

In addition, time compliance can be conceptualized as when an action isperformed or documented, such as what time of day or what day of week.Time compliance may be measured by a time compliance index score. In anexample, a variance or standard deviation or other measure ofvariability of the time of performance with respect to the requestedtime is calculated over a time period. In such a configuration, a highervariability score may indicate less patient compliance. The timecompliance index score may be a function of such a variability score,such as a normalized inverse of the variability score such that a highertime compliance index score indicates a generally more compliancepatient.

Technique compliance may be viewed as how correctly or how completely apatient conducts or performs a requested action. By using one or moreobjective auxiliary measurements, a technique compliance index score maybe derived. Not every requested patient action may be tested fortechnique compliance as some actions are too simple and others do notprovide objective metrics to measure technique.

Patient actions may be detected using an interactive or interrogatorydevice (e.g., a patient monitor or personal computer), one or moreexternal devices (e.g., a weight scale or blood-pressure cuff), one ormore implanted devices (e.g., a cardiac rhythm management (CRM) device,accelerometer, or heart monitor), or any combination thereof. Additionalexamples of external sensors include, but are not limited to, a peakflow monitor, a glucose monitor, an oxygen saturation monitor, or anelectrocardiogram monitor.

Requested patient actions may include one or more actions related toongoing health care or therapy. For example, a patient may be requestedto measure their blood pressure or weight at regular intervals.Requested patient actions may also include non-health care ornon-therapy related actions. For example, a patient could be requestedto report the outside temperature daily at a particular time. Such anaction is not directly related to a patient's health care or therapy,but may be used as a surrogate or indirect measure of compliance.Patients who are generally more compliant to arbitrary instructions mayalso be more compliant to health care directives.

Monitoring one or more patient compliance index scores may provide anindication of a change in physiological or psychological disease state.Patients may be compared to a population of patients to determinewhether they fall outside a particular level of compliance or range ofcompliance index scores (e.g., a median or mode of a patientpopulation). The population of patients may be selected using one ormore of the patient's particular characteristics, such as age, weight,gender, disease risk, current medical status, or the like. In addition,patient compliance scores may be used in auxiliary processes, such as awithin-patient diagnosis, as described above. An acute change in apatient's compliance over time may indicate the onset of a physiologicalor psychological condition or disease state, such as heart failuredecompensation, as an illustrative example. In other examples, a changein a patient's compliance may be indicative of or used for determining alikelihood of a change in a patient's quality of life or a likelihood ofpatient death in a particular time period or time frame.

A patient may be characterized into a class of compliancy. Groupingpatients that are generally more compliant and patients that aregenerally less compliant may be used to determine which patients mayrequire more observation, clearer instruction, or different therapy.

Another use of surrogate measures of patient compliance is to identifyor label data as outliers. In other words, collected patient data, whichmay include subjective response data, measured physiological data, orother detected, measured, or sensed data related to a patient, may beconsidered suspect or viewed with less confidence if the patient'ssurrogate measure of patient compliance is below a threshold acceptablelevel. In an example, patient data (e.g., electrograms, physicalactivity levels, HRV, heart sounds, etc.) recorded around the same timethat the patient compliance score was below a threshold is flagged.Flags may be used as a measure of the quality of the measured patientdata. For example, a patient compliance index score may be based ontimely and consistent patient weigh-ins using an external weight scale.When the compliance index score falls below a threshold, patient weightvalues obtained may be considered suspect and may be weighted less in awithin-patient or between-patient analysis. In another example, when thecompliance index score falls below a threshold, physiological sensordata may instead be given an increased weight on the grounds that poorcompliance can inferentially indicate that the patient may not befeeling well. This may be useful, for example, when the particularphysiological sensor data is believed to be relatively independent ofthe particular patient compliance assessment technique being used.

Several modes of analysis are illustrated in FIGS. 7 and 8. FIG. 7illustrates an example of a method 700 of using sensed patient actionsto determine a level of patient compliance. The method 700 illustratedin FIG. 7 detects and monitors patient actions in response to a request.At 702, one or more occurrences of a patient action are detected.Patient actions may be in response to a request for such an action by aclinician, therapist, or physician. For example, a patient may berequested to log onto a website daily and answer one or more questions,which need not be related to the patient's health or current therapy.When a clinician asks a non-patient health related question, such as “Isit cloudy outside?”, the clinician may be more interested in whether thepatient responded, and when the patient responded, than whether theresponse is correct. In another example, a patient may be requested totake and report their blood pressure daily. Such a request may berelated to the patient's current therapy or health monitoring, but forthe purposes of measuring and determining patient compliance, the valueof the blood pressure reading is irrelevant—the requesting physician orclinician may be more interested in the regularity or proper performanceof the patient's actions. Some requested actions may be relativelysimple, such as pressing a button on a user-interface display daily at aparticular time. Other requested actions may be more complex, such asfor example, accessing and interacting with a particular website.

At 704, a patient compliance index is calculated. In an example, thepatient compliance index is calculated using one or more of a frequencycompliance value, a time compliance value, or a technique compliancevalue. In an example, the patient compliance index is normalized, suchas to provide a range of values from zero (least compliant) to one (mostcompliant). In some examples, the patient compliance index is calculatedusing two or more values in a weighted function. In an example, theweighted function is a function of an aspect of a detected responsivepatient action. For example, the weighted function may focus on the timecompliance of the patient's actions over a period of time. The weightedfunction may weigh more recent occurrences more than less recentoccurrences. In another example, the weighted function is a function oftwo or more aspects of a detected responsive patient action. Forexample, given a patient action, time compliance may be considered moreimportant and thus given a higher weight in the weighted function thantechnique compliance. In another example, different weights aredistributed both temporally and across different aspects of a detectedpatient action. Weight factors may also be related to the number or typeof sensors used, one or more patient characteristics (e.g., healthtrends or risk stratification), or a patient population, in variousexamples.

At 706, the patient index is compared to one or more threshold values.In various examples, the threshold values may be an absolute value, atrended value, a population-based value, or the threshold value may bemanually selected, e.g., by a user, such as a physician. Thresholdvalues may define a minimum or maximum expected value, such that whenthe patient falls under a minimum threshold or exceeds a maximumthreshold value, a resulting particular action or state (e.g., an alertor alarm) may occur. Threshold values may also be used to define aninner or outer range of expected or accepted values.

At 708, if the patient index violates a threshold value or condition,for example when a value is outside of a range bounded by one or morethreshold values, then an alert is generated. The alert may becommunicated to a user, such as a physician, or be used in furtherprocessing, such as in determining an alert score or a composite alertscore, as described above.

The index score and one or more details about the alert state, e.g.,whether an alert was generated, to whom it was communicated if there wasan alert, etc., can also be stored at 710. The compliance index or alertmay be provided to one or more other systems, processes, or devices, forexample to record and maintain a patient history or for qualityassurance evaluation of the system. Recording patient compliance indexscores over a period of time may be advantageous to analyze or evaluateone or more trends in the patient's compliance activity.

While FIG. 7 illustrates a method 700 that emphasizes detecting andmonitoring a single type of requested patient response, FIG. 8illustrates an example of a method 800 of determining a compliance indexover two or more different patient responses. In addition to being anindicator of patient compliance, monitoring more than one patientresponse may be advantageous, such as to determine a secondary physical,physiological, or psychological condition. For example, a patient may berequested to weigh themselves daily and also to report the outdoortemperature using a handheld interrogatory device (e.g., a patientmonitoring device). When the patient fails to weigh themselves overseveral days, but continues to report the outdoor temperature using thehandheld interrogatory device, it may be inferred by the attendingphysician that the patient may be physically unable to get to thebathroom to weigh himself. The inference may be supported by a deducedfact that the patient is still capable of reporting the temperature fromusing the handheld patient monitoring device, which may be situated moreconveniently, such as beside the patient's bed. In such a situation, thephysician may wish to follow up to ensure that the patient is physicallystable. Detecting the presence or absence of data or other trends may beuseful to determine or predict patient problems, such as heart failuredecompensation, loss of cognitive function, or physical incapacity.

At 802, two or more occurrences of different patient responses aredetected. Detection may be automatic or manual. Examples of anautomatically detected patient response includes using a softwareprogram or other programmable device to telephone or email a patientdaily at a particular time and detect a patient response. Other examplesinclude sensors in implanted or external devices to detect things, suchas physical activity levels of the patient, physical location of thepatient (e.g., using a GPS device to detect whether the patient has lefttheir house in a particular time period), or the like. Examples ofmanual detection include requesting that a patient measure themselvesdaily, such as by using a network-enabled weight scale connected to acentralized patient management system, or having a live operator orother personnel call or visit the patient daily to determine whether thepatient was compliant that day.

At 804, for detected occurrences, the occurrence is analyzed at 806.Analysis of the occurrence may be similar to that described withreference to method 700 in FIG. 7. For example, one or more aspects ofthe occurrence may be analyzed, such the time regularity, frequencyregularity, or technique correctness.

At 808, a compliance score is determined for the particular occurrence.The compliance score may be a weighted function of one or more aspectsof the occurrence. The compliance score may also be a weighted functionover time, such as weighing several successive occurrences in aparticular time period.

At 810, the compliance scores of the two or more occurrences ofdifferent patient responses are combined into a composite complianceindex. The composite compliance index may be computed using a weightedfunction. The weights in the weighted function may be static or dynamic.The composite compliance index may be stored and provided to othersystems, processes, or devices.

FIGS. 9A-9F are charts illustrating examples of recorded patient actionsin response to at least one specific request. In the exampleillustrated, the specific request is for the patient to weight himselfdaily unclothed at 7:30 AM. The first chart 900 in FIG. 9A illustratesconceptualized (not real) data illustrating a series of weightmeasurements detected in response to the specific request. Asillustrated, the patient's normal weight is in a range of approximately114 kg and 117 kg. In an example, an allowable daily weight variance isprovided to account for natural weight changes.

The second chart 902 in FIG. 9B illustrates the recorded time of eachweigh-in. In an example, an allowable time variance is provided to allowfor some flexibility in the timing of the patient's responsive action.In another example, any variance from the exact specified time mayresult in a lower compliance score.

The third, fourth, and fifth charts 904, 906, 908 illustrated in FIGS.9C-9E respectively illustrate a trended time compliance score, a trendedfrequency compliance score, and a trended quality compliance score(technique compliance). In an example, the trended time compliancescore, as illustrated in the third chart 904, is computed using theprevious week's worth of recorded patient actions. In an example, thetrended time compliance score is normalized, such as from a score ofzero to one. Here, the specified time to perform the action is 7:30 AM.Using an allowable time variance of ±30 minutes in this example, when apatient perform the requested action (weighing in) at any time between7:00 AM and 8:00 AM, the patient is deemed to be in full compliance withrespect to time. Using the prior seven day's data, the first value 910of trended time compliance is a 1.0 because each of the prior sevenday's weigh-ins were performed within the 7:00 AM to 8:00 AM allowabletime range. When the patient fails to perform the requested actionwithin the allowable range, such as at 912, then the correspondingtrended time compliance score falls, such as at group 914.

The fourth chart 906 in FIG. 9D illustrates a trended frequencycompliance score based on the data in the first chart 900. Similar tothe time compliance scores, the trended frequency compliance score isbased on the previous week's worth of data, in an example. Here, whenthe patient performs the action, a corresponding daily frequencycompliance score is one, and when the patient fails to perform theaction, the corresponding daily frequency score is zero. The trendedfrequency compliance may be calculated as a linear function of theprevious week's daily frequency compliance scores, such as

$\frac{\sum\limits_{j = 0}^{6}{fc}_{j}}{7},$where fc_(j) is the daily frequency compliance score (1 if the patientperformed the requested action and 0 if the patient did not). Asillustrated, the trended frequency compliance score falls off, see group916, when a patient action is not detected, such as at 918, until thepatient has performed the requested action for a full week's time withregularity. The trended frequency compliance score will then be adjustedto a value 920 to indicate full compliance.

The fifth chart 908 in FIG. 9E illustrates a trended quality compliancescore. Quality compliance may also be referred to as techniquecompliance. Some patient actions may be analyzed for such a complianceusing the measurement value or other aspect of the requested patientaction to infer or deduce a level of quality or correct technique usedby the patient when executing the requested action. Similar to thetrended time compliance score and the trended frequency compliancescore, the trended quality compliance score may be based on prioroccurrences of the patient's responsive action. In this example, thewindow or number of occurrences used to calculate the trended qualitycompliance score is illustrated as being five days. Here, the specificinstructions included the instruction for the patient to measure theirweight unclothed. Recognizing data outliers, such as those at 922, whichare abnormally high in comparison to other data points in the firstchart 900, it may be inferred or deduced that the patient improperlywore clothes while weighing in. Thus, the daily quality or techniquecompliance score is lower and corresponding trended quality compliancescore falls off, such as at 924.

One or more of the trended time compliance score, trended frequencycompliance score, or trended quality compliance score, may have anassociated threshold value, such that if the trended compliance scorefalls below the threshold value, an alarm is issued. Threshold valuesare illustrated in the third, fourth, and fifth charts 904, 906, 908 asdashed lines 926, 928, 930, respectively. The threshold may be based ona statistical or probabilistic model (e.g., using a population databaseor previous measurements from a particular patient) or may be maintainedby a user (e.g., a physician or clinician). For example, in somesituations a user may want a higher or lower sensitivity to changes indifferent measures of compliance. Manually raising or lowering thethreshold value for one or more of the trended compliance scores mayallow the user to manage false positive or false negatives (e.g.,specificity or sensitivity) of compliance alerts. A CFAR technique canalso be used, as discussed and incorporated above.

In some examples, a combined compliance score may be calculated, asillustrated in the sixth chart 932 in FIG. 9F. The combined compliancescore may be a weighted function of one or more of the trended timecompliance score, the trended frequency compliance score, or the trendedquality compliance score. In the example illustrated, the combinedcompliance score is a weighted linear function of the trended timecompliance score, the trended frequency compliance score, and thetrended quality compliance score, each with equal weights. In anexample, the combined compliance score may also be trended with respectto time. A threshold value may also be provided (illustrated as dashedline 934), such that if the combined compliance score is calculated tobe less than the threshold value, an alarm is issued.

As an extension of the example illustrated in FIGS. 9A-9F, two or morerequested patient actions may be recorded and analyzed. The combinedcompliance score, as shown in the sixth chart 932, may be a function ofone or more of the time, frequency, or quality compliance scores fromeach of the two or more requested patient actions. One or more of therequested patient actions may be weighed differently from each other inthe combined compliance score. In addition, each element of the combinedcompliance score (e.g., time, frequency, or quality) may also have anassociated weight, which may differ from one another.

Between-Patient Diagnosis

Although monitoring a patient's physiological or other health-relatedindications over time may provide some insight into the patient'shealth-related trends, analysis may be made more complete by including abetween-patient diagnosis technique. Between-patient diagnosis leveragespreviously recorded and documented patient data for the benefit of acurrent patient. By comparing the current patient to a group ofsimilarly situated patients, probabilistic determinations may be made.For example, based on comparisons to a reference group or control groupof patients, a particular patient may be said to be more similar or lesssimilar to the reference group. As another example, using one or moreother comparisons to the reference group, the particular patient may beprobabilistically deemed more or less likely to experience a healthevent in a given amount of time (e.g., a specified “prediction timeinterval”), relative to the reference group. Using one or more suchprobabilistic measurements, a physician may change diagnosis or adjustor adapt therapy to increase the quality of life of the particularpatient. For example, a physician may increase the number of follow upvisits or shorten the length of time between successive follow upvisits, tune one or more thresholds on one or more alert methods, oralter medication to be more aggressive or less aggressive.

In an example, a between-patient technique provides a population-basedstratification of patients according to their risk of a health condition(e.g., heart failure decompensation) within a particular time frame(e.g., three months). For example, a given patient may be classified as“high,” “medium,” or “low” risk when compared to a reference patientpopulation. The technique can include comparison of one or more heartrate variability (HRV) diagnostics of a patient with a model of one ormore similar diagnostics of a reference population. The referencepopulation may include one or more typically, multiple patients, thatmay be similar to the current patient, such as being prescribed withsimilar medical devices or associated with similar therapies. Thebetween-patient technique results in an index value, which may indicatewhether (or a degree to which) the patient is similar to the referencepopulation.

In an example, one or more threshold values are used to categorize orbin the patient into a particular group associated with a risk level orcategory. For example, threshold values may be established usingquartiles, deciles, quintiles, or the like. In other examples, alogarithmic, exponential, or other distribution function (e.g., a Bellcurve) may be used to stratify a patient population into two or morerisk categories or levels. Threshold values may be adjusted, such asperiodically or recurring. Adjustments may be performed automatically ormanually, in various examples. For example, when a reference patientpopulation is changed or replaced, such as when new patients are addedto an existing reference group, one or more threshold values may bemodified to maintain a proper population distribution. Such anadjustment may occur when triggered by a user (e.g., a physician) whohas confirmed the use of the revised patient population reference group.An adjustment to one or more threshold values may occur automatically,such as when a system detects the availability or use of a revisedpatient population reference group.

While examples illustrating the use of HRV diagnostic values aredescribed, other physiological, psychological, or other patientindications may be used to compare a particular patient with a referencegroup. For example, heart rate (HR), physical activity, blood pressure,heart sounds, intracardiac or thoracic or other impedance, or othermetrics may be used for categorization or comparison.

Constructing an appropriate reference group may impact the accuracy orvalue of any predictive calculations based on comparisons between apatient and the reference group. As such, the reference group may beselected based on one or more similarities with the patient in question.Similar patients may include:

-   -   patients who participated in the same controlled study;    -   patients who are managed by the same or similar health provider,        such as the same implant provider or the same therapy provider;    -   patients who are viewed as stable (e.g., did not die in a        particular time, did not decompensate within a particular time,        are compliant in their medication or other prescriptions, report        a high quality of life, or have not used the health care system        in a particular time period);    -   patients with similar age, gender, ethnicity, geography, clinic,        left ventricular ejection fraction (LVEF), New York Heart        Association (NYHA) heart failure classification, HF etiology,        body mass index (BMI), blood pressure, Six-minute walk test        (6MW), quality of life (QoL);    -   patients who have survived for a particular time frame (e.g., 5        years after implant or 6 months after change of therapy),        patients who have not decompensated in a particular time frame        (e.g., in the last 9 months);    -   patients using the same or similar medication;    -   patients with one or more similar co-morbidities or arrhythmia        history;    -   patients with a similar device implant or device implant        history.        This list of similarity characteristics is not meant to be        exhaustive or complete, but merely illustrative of examples of        some characteristics that may be used as parameters to group or        associate patients into a reference group.

Reference group patients may be selected from public or privatedatabases. For example, patients may be selected from a databaseassociated with a remote patient management system, such as LATITUDE® asprovided by Boston Scientific Corporation's Cardiac Rhythm Management(CRM) group of St. Paul, Minn. In addition, reference groups may bestatic or dynamic. Static reference groups may be comprised of patientshaving records that existed in a database or system at the time thecurrent patient enrolled or entered the database or system. Thus, staticreference groups may represent a snapshot of patients who existed in thesystem at a particular time, such as at the time of enrollment of a newpatient. Static reference groups may not be updated. For example, for aparticular diagnostic technique, a snapshot static reference group ofpatients is used to satisfy assumptions made in the analysis of theparticular diagnostic technique. Changes in the static reference groupmay invalidate the results of such a strict diagnostic technique.

Dynamic reference groups may include dynamically updated staticreference groups or true dynamic reference groups. Dynamically updatedstatic reference groups may be updated recurrently or periodically, suchas weekly, monthly, seasonally, or annually. Such an update may create anew static reference group, to be used for a period of time. Dynamicallyupdated static reference groups may also be updated at a triggeringevent. Examples of triggering events include an interrogation of acurrent patient's implantable device, an implantation of a new patientdevice, the introduction of a new patient device (e.g., a release of anew model, firmware, software, or other component of a patient device),the introduction of a new drug, or when a new revision of the referencegroup is approved by an authority, such as the Food and DrugAdministration (FDA). Additional examples of triggering events include adetected change in a patient's health condition, a change of a standardof care, a change in a population statistic (e.g., lifestyle, eatinghabit, education, birth rate, death rate, or habits), or the like.Triggering events may also include one or more user commands to update areference group. The user commands may include one or more parameters,such as patient age; gender; comorbidity; implant type; or otherphysiological, environmental, cultural, or patient-related data. In anexample, the parameters act as a filter that defines a patientsubpopulation, which is used as a dynamically updated patient referencegroup. In various examples, the parameters may be combined using logicalconjunction, disjunction, or both.

A true dynamic reference group typically includes a patient referencegroup that modifies its contents automatically, such as in nearreal-time. For example, a true dynamic reference group may be definedusing one or more parameters, such as those described above, tocharacterize and select a subpopulation of patients. When a patientexperiences a change in a physiological, environmental, or otherpatient-related characteristic, the patient may automatically be addedto or removed from the true dynamic reference group. In effect, in anexample, a true dynamic reference group may be considered a dynamicallyupdated static reference group that is updated when the reference groupstatistic (e.g., distribution) is requested or accessed. In anotherexample, a true dynamic reference group may be viewed as a dynamicallyupdated static reference group that is triggered to update at a smallincrement in time, such as every second, to make the reference groupappear as a nearly real-time, dynamic view of a patient subpopulation.

FIG. 10 illustrates an example of a method 1000 of deriving aprobabilistic index based on a particular patient compared to a patientpopulation. At 1002, one or more physiological indications are received.Examples of physiological indications include sensed cardiac signals,physical activity level, and SDANN or Footprint % indices. Footprint %indices may include a measurement of an area under a 2-D histogram ofheart rate variability of a patient. Physiological indications may bedetected or provided by implanted or external patient monitoringdevices. For example, an implanted cardiac rhythm management device mayinclude electronics, memory, or other components to detect or storeheart rate intervals, implantable electrograms, electrogram templatesfor tachyarrhythmia detection or rhythm discrimination, pressure (e.g.,intracardiac or systemic pressure), oxygen saturation, physicalactivity, heart rate variability, heart sounds, thoracic or intracardiacor other impedance, respiration, intrinsic depolarization amplitude,heart rate, data related to tachyarrhythmia episodes, hemodynamicstability, therapy history, autonomic balance, heart rate variabilitytrends or templates, or trends, templates, or abstractions derived fromsensed physiological data.

At 1004, a patient reference group is determined or otherwise mapped tothe current patient. As described above, the patient reference group maycomprise patients from a pool of patients that share one or moresimilarities with the current patient. Increasing the number ofsimilarities shared between the reference group and the current patientmay increase the quality or accuracy of predictive calculations.Determining a relevant reference group may include considering one ormore other factors, such as age, gender, medication, medical history, orthe like, such as those described above.

At 1006, a reference group dataset is determined. In an example, thereference group dataset includes patient data of patients in thereference group, where the patient data is substantially similar to thephysiological indications received at 1002. For example, if at 1002, apatient's physical activity levels are being monitored and reported byan internal or external patient device, then at 1006, patient dataassociated with physical activity level from the patient reference groupis selected as the reference group dataset.

At 1008, a model of the reference group dataset is determined. In anexample, the model is a probabilistic model and calculated using aprobability function. In a further example, the probability functionincludes a cumulative distribution function (CDF). For example, themodel may include a series of 1-dimensional (1D) empirical cumulativedistribution functions of the reference group's weekly-averagedactivity, SDANN, and Footprint % values. As another example, the CDF mayinclude a single joint multivariable CDF with either a diagonal or fullcovariance matrix. In another example, the probability function includesa probability distribution function (PDF). In an example, aprobabilistic model may include a series of 1-D probability distributionfunctions (PDF), where a particular PDF models a distinct parameter. Inanother example, the model may include a single joint multi-dimensionalPDF, where each dimension models a distinct parameter. For example, aPDF may include a joint multivariable PDF with either a diagonal or fullcovariance and may be estimated over the reference group patients'weekly-averaged activity, SDANN, and Footprint % values. Otherphysiological parameters may be used in the modeling and comparison,such as average heart rate, maximum heart rate, minimum heart rate,respiration rate, amplitude of S3 heart sound, or pulmonary arterypressure.

At 1010, the current patient's received physiological value can be usedto determine an index value based on the model of the reference groupdataset. The index value may be calculated periodically or recurrently,such as daily, weekly, or monthly, such as by using average values forthe periodic or recurrent time interval. In an example, 1-dimensionalCDFs can be used as “look up tables” to determine what percentage ofreference group patients had physical activity levels less than or equalto the current patient's physical activity level. A similar process maybe used with SDANN and Footprint % values. For each percentile, valuesnear 0.5 can indicate that the patient is in the 50^(th) percentile ofthe reference group (e.g., the patient is similar to the referencegroup), while values near 0 or 1 indicate that the patient is dissimilarto the reference group. The individual indices may be combined into acomposite index, such as, for example, by multiplying, adding, orotherwise mathematically combining the individual indices.

In another example, a probability distribution function (PDF) can beused to model the reference group dataset. For example, a PDF may beconstructed using the reference patients' activity, SDANN, and Footprint% values. The current patient's physiological values can be compared toan estimated PDF to determine the patient's index value. The index valuemay include the negative log-likelihood that the current patient's setof activity, SDANN, and Footprint % values belong to the PDF. In certainexamples, the index value may also be the area under the PDF enclosed by(or outside of) an equiprobable contour that represents the probabilitythat the current patient's set of values belong to the estimated PDF. Ineither case, a low (or high) index value indicates how similar (ordifferent from) the current patient is compared to the reference group.

The index value may be advantageous to provide easier comparison betweenpatients, provide a reference value that is easy to interpret, provideeasier identification of any outlier values, or provide more insightinto one or more correlations between patient physiological indicationsand probabilistic diagnoses. In some examples, the index value mayindicate how likely a patient is to enter or recover from a diseasestate in a particular amount of time. As an illustration, the indexvalue may be interpreted to indicate the likelihood of a patient toexperience heart failure decompensation in the next six months, such asrelative to other patients in the patient reference group. For example,Hazard ratios or Cox Proportional Models may be used to determine such alikelihood. In other examples, the index may be used to indicate howlikely a patient is to experience a change in health, such as anincrease or decrease in quality of life, or a likelihood of death in aparticular timeframe.

FIGS. 11A-11C illustrate examples of a physical activity cumulativedistribution function (CDF) chart 1100 in FIG. 11A, an SDANN CDF chart1102 in FIG. 11B, and a Footprint % CDF chart 1104 in FIG. 11C. In FIG.11A, the activity CDF chart 1100 includes an activity value 1106 alongthe x-axis and an activity index 1108 along the y-axis. The activityvalue 1106, in an example, represents the percentage of time a patientis considered active using a threshold, which may be based on heartrate, blood pressure, accelerometer, or one or more other indications ofphysical activity. The activity index 1108 represents the percentile ofa particular patient with a particular activity value 1106. For example,a patient with an activity value 1106 of 10 has a corresponding activityindex 1108 of approximately 0.62, which indicates that the patient is inthe 62^(nd) percentile of active patients, e.g., the patient is moreactive than 62% of the patients represented.

Similarly, in FIG. 11B, the SDANN CDF 1102 includes a standard deviationvalue along the x-axis 1110 and a SDANN index 1112 along the y-axis. Inan example, the SDANN index 1112 represents the percentage of patientsthat have a SDANN value equal to or less than the corresponding standarddeviation value 1110.

In FIG. 11C, the Footprint % CDF 1104 maps a footprint percentage 1114against a footprint index 1116. In an example, the footprint index 1116represents a percentile of patients who have a footprint percentagevalue equal to or less than the corresponding footprint percentage 1114.

FIG. 12 is an example of a probability distribution function chart 1200that illustrates reference group patients' physical activity levels. Thechart 1200 includes activity values on the x-axis and a percentage ofpatients who have the corresponding activity on the y-axis. To determinean activity index for a particular patient, the area under theprobability distribution function (PDF) curve is calculated. In theexample illustrated, by using equations that describe the probabilitydistribution function chart 1200, it can be calculated that a patientwith an activity level of 14 corresponds to a point 1202 on the curve.The 1-D activity PDF shown in FIG. 12 identifies a pair of points withequivalent probability density that defines an interval of integration.By analogy, a 2-D density would yield sets of points with equivalentprobability densities or contours that would define areas ofintegration. In the example illustrated in FIG. 12, point 1202 and point1204 share a common probability density. Using the two points 1202,1204, an area 1206 under the PDF is defined. In an example, the activityindex is equal to the area 1206 under the PDF. Using the calculatedactivity index may provide advantages, including easier comparisonbetween several patients or easier communication of a patient status tothe patient or other medical professionals.

Inter-Relationship between Within-Patient Diagnosis and Between-PatientDiagnosis

A between-patient analysis may provide a more long-term indication of apatient's risk compared to a population. In contrast, a within-patientanalysis may provide a more short-term indication of acute changes in apatient's health. Thus, it may be advantageous to use one analysis totune performance of another analysis. For example, a between-patientanalysis that includes a large number of patients in the population mayprovide a sufficient confidence that a particular patient is high or lowrisk for the occurrence of a particular physiological condition. If thepatient is considered high-risk, then one or more parameters of awithin-patient analysis may be modified. For example, sampling timingintervals may be shortened to detect acute changes quicker, thresholdvalues may be revised, or a probability distribution model may beselected based on the type or severity of the population-based risk. Incontrast, if the patient is considered low-risk or lower risk, then awithin-patient analysis may not be considered necessary. Alternatively,the within-patient analysis in such a situation may be revised to lessbe invasive or have reduced sensitivity and increased specificity (e.g.,to reduce false alarms). Such a system may allow physicians to stratifypatients according to their long-term risk using the between-patienttechnique and keep a closer watch for acute changes in patients withhigher risk using the within-patient technique.

In an example, a within-patient decompensation detection technique maybe enabled or disabled when a low or high index value is returned from abetween-patient risk stratification technique. FIG. 13 is a diagram 1300illustrating an example of control and data flow between patientanalysis processes. Sensor data 1302 is received and analyzed by abetween-patient diagnostic technique 1304, such as one described above.The between-patient diagnostic technique 1304 outputs an index 1306indicative of a risk or likelihood of a patient experiencing a diseaseor other health concern similar to that of the population used in thebetween-patient diagnostic technique 1304. A control module 1308receives the index 1306 and compares it to a risk threshold. In anexample, the risk comparison results in a tri-state output, such as“low,” “medium,” and “high” risk in comparison to a threshold value or arange of threshold values. When the index 1306 is associated with a lowrisk, then a corresponding within-patient alert (WPA) technique isdisabled 1308. When the index 1306 is associated with a medium risk,then no change is made-if the WPA technique was enabled, then it remainsenabled, and if the WPA technique was disabled, then it remainsdisabled. When the index 1306 is associated with a high risk, then theWPA technique is enabled. In an example, the WPA technique is enabled ordisabled automatically. In another example, a user (e.g., an attendingphysician) may be notified of the suggested change in WPA state and maythen manually or semi-automatically enable or disable the WPA technique.

-   -   Example: After a hospitalization, cardiac diagnostics may        stabilize due to the effect of a drug therapy resulting in a        lower index value (result of a between-patient diagnostic        technique). In light of the lower index value, the        within-patient technique may no longer be considered necessary.        Thus, the within-patient technique may be disabled automatically        or manually to reduce false alarms that may result from acute        changes in patient data.    -   Example: After an implant procedure, if the index value from a        between-patient technique is high enough (e.g., greater than a        threshold value), it may imply that the patient is sufficiently        different from a reference group comprising stable CRT-D        patients that a physician may choose to maintain a closer watch        on the patient. To do so, the physician may enable        within-patient technique to alert the physician of acute changes        in diagnostic parameters.

In an example, one or more parameters of a within-patient technique maybe enabled, disabled, or modified based on the result of abetween-patient technique. For example, an acute detection threshold maybe adjusted based on one or more population-based risk assessments. Asanother example, a measurement probability distribution function (PDF)model may be selected based on the population-based result (e.g., usinga Gaussian or lognormal PDF model).

FIG. 14 is a diagram 1400 illustrating an example of control and dataflow between patient analysis processes. Similar to the system describedin FIG. 13, based on an index value 1402, risk can be assessed with atri-state output. In this illustration, when the risk is considered low,then one or more parameters in the within-patient technique are modifiedto make the technique more specific and less sensitive 1404. When therisk is considered high, then the technique is made more sensitive andless specific by adjusting the one or more parameters 1406. Finally,when the risk is considered medium, then the one or more parameters aremaintained at their current values 1408. Parameters may include weightsin a weighted function (weighting factors), models used for patientcomparison, one or more threshold values, or the like. Parameters mayalso include variables that control conditional states (e.g., controlflow), sample resolution (timing), frequency of assessment, pattern ofassessment (e.g., time of day, sequencing of multiple assessments), orthe like. For example, one or more parameters may be automaticallydetermined or provided by a user (e.g., a physician or clinician) toindicate which of one or more analysis processes are evaluated and inwhich order after a preceding analysis is completed. Controlling theselection and arrangement of the analysis processing may be advantageousto refining the analytical result or reducing processing errors (e.g.,false positive or false negative indications).

By automatically or manually adjusting the parameters of thewithin-patient technique, false alerts may be reduced or minimized,which may allow patients to be managed more efficiently. In an example,some parameters are adjusted automatically. In another example, one ormore proposed changes to parameters are presented to a user, forexample, an attending physician, who then may either permit or denychanges to the parameters.

-   -   Example: If a between-patient stratifier technique indicates        that SDANN has a higher sensitivity for a particular patient        compared to minimum heart rate (HRMin), then a within-patient        technique may be modified to assign a higher weight to an SDANN        parameter in a weighted function.

In certain examples, one or more performance parameters of a firsttechnique, such as a between-patient stratifier, may be adjusted toaffect the false positives, false negatives, specificity, sensitivity,positive predictive value, negative predictive value, number of falsepositives per year of a second technique, such as a within-patienttechnique.

As described above, sensitivity generally refers to the ability of thedetection scheme to effectively detect a particular result. Sensitivitycan be expressed with the formula: sensitivity=(true positives)/(truepositives+false negatives). Thus, a higher sensitivity generallyindicates that an analysis correctly characterizes more true positivesor eliminates false negatives.

Specificity generally refers to the ability of the detection scheme toavoid improper classifications. Specificity can be expressed with thefunction: specificity=(true negatives)/(true negatives+false positives).Thus, a higher specificity generally reflects more accurateclassification of true negatives or reduction of false positives.

Positive predictive value (PPV) generally refers to the ability of thedetection scheme to accurately produce correct positive results. PPV canbe expressed with the function: PPV=(true positive)/(truepositives+false positives). Thus, PPV exhibits a ratio of correctpositive indications.

Negative predictive value (NPV) generally refers to the ability of thedetection scheme to accurately produce correct negative results. NPV canbe expressed with the function: NPV=(true negatives)/(truenegatives+false negatives). Thus, NPV exhibits a ratio of correctnegative indications.

False positives (FP) per year is a ratio of false positive indicationsover one or more years. False positives per year can be expressed withthe function: FP/yr=(FP in one or more years)/(number of years).

In an example, a within-patient technique may be used to influence abetween-patient technique. For example, the between-patient techniquemay be enabled, disabled, or have one or more parameters modified orenabled based on the results of the within-patient technique.

FIG. 15 illustrates a cross-feedback configuration of patient analysisprocesses. Patient data 1500 is received at an analysis system 1502. Inan example, the analysis system includes a remote patient managementsystem, such as LATITUDE®. A between-patient index technique 1504 or awithin-patient technique 1506 may use the received patient data 1500 tocalculate an index 1508 or an alert 1510, respectively. In an example,the index 1508 indicates how similar a patient is to a patientpopulation (e.g., reference group). In an example, the alert 1510indicates an acute change in patient physiological parameters. The index1508 and the alert 1510 are received at a control system 1516. In anexample, the control system 1516 is part of the same system as theanalysis system 1502, e.g., LATITUDE®. In other examples, the controlsystem 1516 and the analysis system 1502 are in separate devices. Forexample, the analysis system 1502 may be located in a programmer, whilethe control system 1516 may be located at a centralized patientmanagement server. A first module 1512 in the control system 1516determines whether to modify the within-patient technique 1506 based onthe calculated index 1508. A second module 1514 in the control system1516 determines whether to modify the between-patient index technique1504 based on the alert 1510. In either case, examples of themodifications may include enabling, disabling, initializing, ormodifying one or more parameters of the corresponding technique.

In another example, three or more diagnostic techniques are configuredto interact with each other. For example, a first between-patientdiagnostic technique may be configured to focus on physical activitylevels, a second between-patient index may be configured to focus onheart rate variability, and a third within-patient diagnostic techniquemay also be available. The results of the within-patient diagnostictechnique (third technique) may affect one or both of thebetween-patient techniques (first and second). In other examples, two ofthe techniques may be configured to affect the third. In other examples,one technique may be used to determine which subsequent technique isused or in what order subsequent techniques are performed. In such aconfiguration, the collection of techniques may be viewed as a statemachine. Creating a matrix or “web” of one or more permutations orcombinations of between-patient or within-patient diagnostic techniquesmay provide higher efficiency in diagnosis or fewer false positive orfalse negative indications.

Physician Feedback

In some situations, diagnostic techniques, such as those describedherein, may result in false positive or false negative indications. Forexample, false indications may occur when a technique is firstinitialized to a general state before the technique has been revised ortuned for a particular patient. To reduce the number of falseindications and improve accuracy, it may be advantageous to allow amedical professional to monitor and control such diagnostic techniques.

FIG. 16 is a dataflow diagram illustrating an example of a physicianfeedback process. Patient data 1600 is communicated to a control system1602. Patient data 1600 may include physiological data, environmentaldata, or subjective patient responses, in various examples. In anexample, the control system 1602 includes some or all of the componentsdescribed in 108 (FIG. 1). In the example illustrated in FIG. 16, thecontrol system includes a storage device 1604 and an operating device1606. The storage device 1604 may be configured as a database, a filestructure, or other storage means. The storage device 1604 typicallyincludes a patient data file 1608, a physician data file 1610, andpatient diagnostic routine file 1612.

The patient data file 1608 may include historical physiological datasuch as in raw or summarized format, historical subjective responsivepatient data, one or more alerts generated from one or more patientdetection techniques, trending data, extrapolated data (e.g., minimum,maximum, or median patient-related values for a particular timeframe),or other patient-related information (e.g., patient identificationinformation, hospitalization information, historical automatic orphysician diagnoses, etc.).

The physician data file 1610 may include physician notes or commentsrelated to a particular patient, physician input (as described infurther detail below), prescribed therapies, or other physician-relatedinformation.

Patient diagnostic routine file 1612 may include programmatic code orother structures that control or enable the decisional process of anautomated patient evaluation. Patient diagnostic routine file 1612 mayalso include variables, such as threshold values, weighting factors, orother parameters used during the execution of patient diagnosticroutines.

The operating device 1606 may include one or more computers or otherprogramming devices to control the execution of patient diagnosticroutines 1614. In an example, the operating device 1606 may accesspatient data from the patient data repository 1608, initialize one ormore patient diagnostic routines 1614 using parameters stored in thepatient data file 1608 or the patient diagnostic routine file 1612,execute the patient diagnostic routines 1614, and store results in thepatient data file 1610 or the patient diagnostic routine file 1612.

At some time, a physician or other medical professional may access thecontrol system 1602 and receive patient-related data 1616.Patient-related data 1616 may include physiological data, test results,summary data, patient diagnostic parameters, patient therapies, or otherpatient data stored in the patient data file 1608 or the patientdiagnostic routine file 1612. The physician may have an opportunity tointerview or examine the patient, such as during a patient visit 1618.Using the observation, interview, or other information, the physicianmay provide feedback 1620 to the control system 1602. In an example, thephysician may provide physician input (e.g., feedback 1620) to thecontrol system 1602 using an observation, interview, examination, orevaluation of a patient or patient-related data. Such input may beindependent from a contemporaneous result generated at the controlsystem 1602, such that the physician may not have reviewed test resultsor may not have been provided with test results in the patient-relateddata 1616. An independent evaluation of a patient, not biased by aresult generated by the control system 1602, may advantageously providea “gold standard” or truth standard, by which the control system 1602may adapt its methods or processes to be more accurate when compared tothe physician's assessment.

In some examples, a physician or clinician may provide input or feedbackusing a terminal, for example as illustrated at 112 (FIG. 1). In someexamples, a physician or clinician may provide input to an electronicmedical records system 1622. Some or all of an electronic medical record1624 (EMR) stored at the electronic medical records system 1622 may thenbe imported to control system 1602. Portions or all of physicianfeedback 1620 may be stored in the physician data file 1610. In anexample, the operating device 1606 may use physician feedback 1620 toalter or adjust the execution of one or more patient diagnostic routines1614.

FIG. 17 illustrates an example of a feedback loop between a centralsystem and a physician. At some time, patient data is received 1700. Thepatient data is analyzed 1702 by one or more patient diagnosticroutines. Results of the analysis are stored 1704. A physician orclinician may access and review 1706 the stored results. The physicianor clinician may provide feedback 1708. The feedback may be in the formof a verification (e.g., correct or incorrect result) or one or morecommands (e.g., increase specificity or decrease threshold of aparticular patient diagnostic routine), in various examples. Thefeedback may be an independent assessment of a patient in an example. Inexamples, the feedback message may be in the form of one or morestandardized languages (e.g., eXtensible Markup Language (XML)) or in astandardized format (e.g., comma-separated file (.csv)). Using thephysician or clinician's feedback, one or more parameters of theanalysis are modified 1710, which may affect later execution.

FIG. 18 is a flowchart illustrating an example of a method 1800 of usingphysician feedback to modify the execution of patient analysis routines.At 1802, patient data is received. Patient data may originate from oneor more sources, including sensed physiological data from one or moreimplanted or external monitoring devices, patient response data from aninteractive or interrogatory device, or health data obtained during anoffice visit or other examination or interview with a medicalprofessional. Patient data may also be retrieved or received from anexternal data source, such as an electronic medical records database.

At 1804, the patient data is analyzed with one or more patientdiagnostic analyses, such as those described above (e.g., within-patienttechnique or between-patient technique). At 1806, the results of theanalysis are provided to a user. In an example, the results areautomatically forwarded to a user when certain conditions exist, forexample, when an alert has been generated. In another example, theresults are stored for later access by a user.

At 1808, a response is received from the user. The response may includea verification message in an example. The verification message mayindicate that the results of the analysis were correct or incorrectbased on further investigation by the user, for example. In anotherexample, the response may include one or more user directives. The userdirective may occur alone or in combination with a verification message.User directives may include increasing or decreasing an analysis'sensitivity or specificity; raising, lowering, or providing a particularvalue for a threshold or other parameter; or increasing, decreasing, orproviding a particular value for an importance or ranking of a sensor ormeasurement. Further examples of user directives are described below.

At 1810, one or more aspects of patient diagnostic analyses are modifiedor adjusted using the response. Modifications may include enabling ordisabling an analysis, increasing or decreasing one or more weights in aweighted function associated with an analysis, or modifying an alertdetection technique (e.g., by raising or lowering a threshold). Othermodifications may be implemented, such as choosing one predictive ordiscrimination technique over another or choosing which techniques touse together. For example, in the context of tachyarrhythmiadiscrimination and detection, a physician may decide to use amorphology-based discrimination algorithm (e.g., Rhythm ID) over aninterval-based discrimination algorithm (e.g., one-button detectionenhancement (OBDE)). As another example, in the context of heart failuredecompensation detection or prediction, a physician may choose to blendthe results of a pulmonary edema detection with an electricaldysynchrony detection.

FIG. 19 is an example of a user-interface to allow a medicalprofessional to submit input or feedback to a control system. In theexample illustrated, a medical professional may provide an indication ofwhether a heart failure patient is decompensating. Such an indication isprovided independent from any result calculated from the control system.For example, a physician may independently examine or interview apatient and derive a diagnosis without referring to a diagnosisgenerated by the control system. The indication need not be tied to aparticular diagnostic analysis. For example, the physician may providean indication that may be related to one or more within-patientdiagnostic techniques and/or one or more between-patient diagnostictechniques. In various examples, the medical professional may bepresented an input to provide one or more health characterizations(e.g., aspects of decompensation, arrhythmia, weight gain, bloodpressure), some of which may be used by the control system (e.g., 1602in FIG. 16) to modify a parameter or other aspect of a patientdiagnostic technique, or a sensor's detection process.

FIG. 20 is a control flow diagram illustrating an example of aninteraction between a user-interface system 2002 and a control system2004 in accordance with the user-interface illustrated in FIG. 19. In anexample, the user-interface system 2002 is incorporated into a userterminal, such as illustrated in FIG. 1 at 112. In an example, thecontrol system 2004 is incorporated into a remote server system, such as108 in FIG. 1. In the example illustrated in FIG. 20, data 2006 isreceived by the control system 2004 and analyzed by a within-patientanalysis 2008, such as an analysis described herein. A composite alertscore is evaluated and compared to a threshold value (Th). If thecomposite alert score is greater than the threshold (Th), then thestatus is presented to a physician interface 2010, such as for display.In examples, the physician interface 2010 may include a computerterminal, an electronic medical records system, or other inputmechanism. A physician may make an independent determination of thepatient's status, for example during an office visit or during atelephonic patient interview. The physician may then provide theindependent determination using the interface, such as an interfaceillustrated in FIG. 19. The independent determination may be performedasynchronously with contemporaneous evaluations performed by the controlsystem 2004 or other systems, such that, for example, the independentdetermination may occur before, during, or after a particularwithin-patient analysis 2008 has been evaluated. The independentevaluation may rely on, at least in part, data similar to that receivedby the control system 2004, such as data 2006, or may use independentlyobtained data, such as data obtained during a patient examination, ormay use a combination of data sources. Whatever the source of data, theindependent evaluation is typically made without reference toautomatically determined results, such as results of within-patientanalysis 2008. In an example, the independent evaluation is stored at anelectronic medical records store and later communicated to the controlsystem 2004 in the form of an assessment message.

The independent determination may take the form of an assessment message2012. One or more assessment messages 2012 are communicated to averification module 2014 in the control system 2004. In variousexamples, the assessment message 2012 may be formatted using astandardized interface language, such as XML, or in a standard fileformat, such as comma-separated values (csv) or a tab delimited format.The verification module 2014 also has access or is provided one or moreaspects of the analysis 2008, such as current threshold values, currentsensors used, or current CAS value. The verification module 2014 mayinclude one or more programmatic modules, such as software programs, tocompare the physician's assessment message 2012 with the output of theanalysis 2008. For example, when the physician indicates that thepatient is decompensating, if the results of the analysis 2008 indicatethat the patient is more likely to decompensate, then the verificationmodule 2014 generates a verification message 2016 indicating that theresult of the analysis was correct. In various examples, theverification message 2016 may be formatted using a standardizedinterface language, such as XML, or in a standard file format, such ascomma-separated values (csv) or a tab delimited format. However, if thephysician indicates that the patient is not decompensating, then theverification module 2014 generates a verification message 2018indicating that the result of the analysis was incorrect.

The verification message 2016, 2018 is received by a control messagemodule 2020. The control message module 2020 also has access to or isprovided with one or more aspects of the analysis 2008. The controlmessage module 2020 may include one or more programmatic units, such assoftware, hardware, or a combination of both, containing instructions todetermine what type of modification if any, is communicated to theanalysis 2008. For example, when the within-patient analysis 2008indicated an alert state and the verification message 2018 indicatesthat the result was incorrect, then in an example, the control messagemodule 2020 generates a control message 2022 to reduce the sensitivityof the analysis and the control system 2004 may then increase thethreshold value 2014 to make the analysis 2008 more specific in laterevaluations. By increasing the threshold value and making the analysismore specific, the physician may affect the analysis to reduce falsepositives in later evaluations. In certain examples, the control messagemodule 2020 may have access to or be provided with one or moreparameters that influence which control message is generated in aparticular situation. For example, if an analysis is incorrect and thethreshold value has been increased several times, then the controlmessage module 2020 may generate a control message 2024 indicating tomaintain the current threshold value.

In a similar fashion, if the composite alert score does not exceed thethreshold, then that result may also be presented to the physicianinterface 2010. The physician may make a similar independent evaluationof the patient's status and submit an assessment message 2012 to theverification module 2014 in the control system 2004. The verificationmodule 2014 then compares the physician's independent evaluation,contained in the assessment message 2012 with one or more aspects of theresult of the analysis and generates a verification message 2026. Theverification message 2026 is then communicated to the control messagemodule 2020 and a control message 2028, 2030 is generated. The controlsystem 2004 may use the control message 2028, 2030 to decrease thethreshold 2032 or keep the same threshold 2034, in certain examples. Forexample, if the physician indicates that the patient is notdecompensating, then the verification module 2014 confirms that thephysician's diagnosis is consistent with the result of the analysis 2008and no change is made 2034 to the threshold value. However, if thephysician determines that the patient is decompensating, then theverification module 2014 may communicate a verification message 2026indicating that the analysis was incorrect and the threshold value maybe decreased 2032 to increase the sensitivity of the analysis in laterevaluations. By increasing the threshold value and making analysis moresensitive, the physician may affect the analysis to reduce falsenegatives in later evaluations. As with previously described case, thecontrol message module 2020 may determine that decreasing the thresholdis either impossible (e.g., due to a lower limit of an analyticaltechnique or a sensor's particular capabilities) or impracticable, andin such a case, the control message module 2020 may generate a “NoChange” message 2030.

FIG. 21 is an example of a user-interface to allow a medicalprofessional to submit input or feedback to a control system. In theexample illustrated, a medical professional may provide an indication ofwhether a particular result of a diagnostic analysis is correct. In anexample, a user is provided with the results of a particular analysis(e.g., heart failure decompensation risk) along with one or more patientphysiological indications (e.g., heart rate intervals, implantableelectrograms, electrogram templates for tachyarrhythmia detection orrhythm discrimination, pressure (e.g., intracardiac or systemicpressure), oxygen saturation, physical activity, heart rate variability,heart sounds, thoracic or intracardiac or other impedance, respiration,intrinsic depolarization amplitude, heart rate, data related totachyarrhythmia episodes, hemodynamic stability, therapy history,autonomic balance, heart rate variability trends or templates, ortrends, templates, or abstractions derived from sensed physiologicaldata). The user may then evaluate the patient's condition and determinewhether the results of the analysis are correct. Conceptually, in anexample, the user takes the place of the verification module 2014 inFIG. 20.

FIG. 22 is a control flow diagram illustrating an example of aninteraction between a user-interface system 2202 and a control system2204 in accordance with the user-interface illustrated in FIG. 21. In anexample, the user-interface system 2202 is incorporated into a userterminal, such as illustrated in FIG. 1 at 112. In an example, thecontrol system 2204 is incorporated into a remote server system, such as108 in FIG. 1. In the example illustrated in FIG. 22, data 2206 isreceived by the control system 2204 and analyzed by a within-patientanalysis 2208, such as within-patient analysis described herein. Acomposite alert score is evaluated and compared to a threshold value(Th). If the composite alert score is greater than the threshold (Th),then the status is presented to a physician interface 2210, such as fordisplay. In examples, the physician interface 2210 may include acomputer terminal, an electronic medical records system, or other inputmechanism. A physician may use the provided information to confirm theresults of the analysis. Unlike the situation illustrated in FIG. 20,the physician has foreknowledge of a result of the automated analysis,such that a patient evaluation is performed in response to the resultand furthermore, to confirm the result. The physician may then providethe confirmation determination using the interface, such as an interfaceillustrated in FIG. 21. The physician's determination is communicatedusing a verification message 2212 in certain examples. In variousexamples, the verification message 2012 may be formatted using astandardized interface language, such as XML, or in a standard fileformat, such as comma-separated values (csv) or a tab delimited format.Similar to the operation illustrated in FIG. 20, the control system 2204can use the verification message 2212 to generate one or more controlmessages 2214, which may direct the control system 2204 to modify theexecution of the analysis 2208.

FIG. 23 is another example of a user-interface 2300 to allow a medicalprofessional to submit feedback to a control system. In FIG. 23, thephysician is provided controls 2302, 2304, 2306 to adjust thesensitivity of a patient analysis. When a physician activates one of thecontrols 2302, 2304, 2306, a control message is generated andcommunicated to the control system, in an example. The user-interfacemay be accessed, for example, during a patient evaluation where aphysician has made an independent determination of the patient's status.If the physician concurs with the automatic patient analysis, then thephysician may activate the “No Change” control 2306. If the physicianbelieves that the patient analysis is incorrect and indicating a falsepositive, then the physician may decide to reduce the sensitivity of theanalysis and activate the “Less Sensitive” control 2302. On the otherhand, if the physician believes that the patient analysis is incorrectand indicating a false negative, then the physician may wish to increasethe sensitivity of the analysis and active the “More Sensitive” control2304. In other examples where multiple patient analysis techniques areused, a separate set of controls may be associated with each patientanalysis technique and presented to the physician. In such aconfiguration, the physician may then have control over each analysis.In other examples, a single set of controls, such as those illustrated,are presented and may control multiple patient analysis techniques in anaggregate configuration. In addition, while controls that may be used tomodify an algorithms sensitivity are illustrated in FIG. 23, in otherexamples, other controls may be provided to a user to control aspects ofperformance measures such as a false positive rate, a positivepredictive value, a negative predictive value, or the like.

FIG. 24 is a control flow diagram illustrating an example of aninteraction between a user-interface system 2402 and a control system2404 in accordance with the user-interface illustrated in FIG. 23. Basedon the result of the within-patient analysis 2406, the physician maydetermine that the result is incorrect and lower the sensitivity 2408 orraise the sensitivity 2410, depending on whether the incorrect result isperceived as a false positive of false negative, respectively. If thephysician agrees with the within-patient analysis, then no change isindicated, such as in control messages 2412 and 2414. Control messages2412, 2414 may be formatted using a standardized interface language,such as XML, or in a standard file format, such as comma-separatedvalues (csv) or a tab delimited format.

FIG. 25 is another example of a user-interface 2500. In FIG. 25, a useris provided one or more controls 2504 to activate or deactivate one ormore sensors associated with a patient analysis technique. In theexample illustrated, one or more sensors are associated with a heartfailure decompensation evaluation. A user (e.g., a physician orclinician) may use the controls 2504 to manage whether each sensorresult is used in the patient analysis (e.g., within-patient analysis).Controlling such aspects of the patient evaluation may be advantageousfor physicians that wish to dismiss particularly unfavorable sensors oremphasize particularly favorable sensors for a particular patient. Forexample, a physician may have determined during their practice that aparticular sensor is less determinative or less accurate when used in aparticular patient's evaluation. Using controls illustrated in FIG. 25would allow such a physician to remove such a sensor from the calculusof such a patient's status.

Additionally, the importance, or weight, of each sensor may be providedby the user by manipulating the importance controls 2502. The importancecontrols 2502 may be presented as a dropdown control containing theallowable range of values indicative of importance. In an example, eachsensor may be associated with a default control, which may be indicatedin the importance control 2502.

FIG. 26 is a control flow diagram illustrating an example of aninteraction between a user-interface system 2602 and a control system2604 in accordance with the user-interface illustrated in FIG. 25. Theuser may send one or more control messages 2606A, 2606B to change sensorweights or activate or deactivate particular sensors associated with apatient analysis.

FIG. 27 is another example of a user-interface 2700 to control one ormore sensors. For example, one or more controls may be provided tomodify threshold values, modify sensitivity using general labels (e.g.,“More Sensitive” or “Less Sensitive”), change the type of thresholdcomputation used (e.g., an absolute value or a percent change from abaseline), or change a detection technique used by a particular sensor.In the example illustrated in FIG. 27, threshold controls 2702 areprovided to a user to set threshold values, such as a function of apercent change from a particular value (e.g., a baseline value or anarbitrary initial value). In addition, sensitivity controls 2704 areprovided so that a user may generally set a particular sensor to be moreor less sensitive. The sensitivity controls 2704 may be configured toindicate a current setting to the user, such as using bold face,coloring, or other graphical or textual details that display to the userthe current setting. In the example shown, when a user changes athreshold value to be higher than the current setting, thus decreasingthe sensitivity, the general sensitivity control 2704 associated withthe changed threshold control 2702 has its presentation altered toreflect the reduced sensitivity. Similarly, when a user selects ageneral sensitivity control 2704, a corresponding threshold value may beindicated in the associated threshold control 2702.

FIG. 28 is a control flow diagram illustrating an example of aninteraction between a user-interface system 2802 and a control system2804 in accordance with the user-interface illustrated in FIG. 27. Theuser of the user-interface system 2802 may send one or more controlmessages 2806 to the control system 2804 to change one or more thresholdvalues associated with one or more sensors, change the sensitivity ofone or more sensors, manage the detection techniques used on one or moresensors, or perform other management tasks as described with regard tothe user-interface in FIG. 27. In an example, the control system 2804may receive unmodified, sensed data 2808 from one or more sensors 2810.The control system 2804 may then analyze the data 2808 and set one ormore alerts using the modified threshold values, sensitivity levels, orother user-provided inputs, and ultimately derive the composite alertscore. In other words, the control system 2804 may retain theuser-provided information and manage the alerts local to the controlsystem 2804. In another example, the control system 2804 may communicatethe threshold values, sensitivity levels, or other user-providedinformation to one or more sensors 2810 corresponding to the sensorspresented in a user-interface, such as in FIG. 27. In such an example,each sensor 2810 may then modify its own internal detection algorithmand provide appropriate alerts using the new threshold values, forexample.

Some of all of the user-interfaces described in FIGS. 19, 21, 23, 25, 27may be combined in various combinations or permutations to grantdiffering scopes of control to a user. Additionally, otheruser-interfaces not illustrated may be provided to a user to controlother aspects of patient analysis techniques, such as analysis blending,sensor blending, timing intervals of sensor fusion over time, sensorsettings, detection thresholds, selected population groups, or the like.

As described above, centralized data may be advantageous for severalreasons. For example, physicians may be able to share data easier in thesituation where patients see several health care providers who are notmembers of the same medical practice and thus, does not have access toeach other's EMR database. In addition, centralized data may providegreater insight into patient health trends when using systems andmethods as described herein.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments (and/or aspects thereof) may be used in combination witheach other. Many other embodiments will be apparent to those of skill inthe art upon reviewing the above description. For example, although thedescription describes a particular example in which information isprovided to a medical practice, in other examples, one or more otherusers obtain such information using the present systems and methods. Thescope of the invention should, therefore, be determined with referenceto the appended claims, along with the full scope of equivalents towhich such claims are entitled. In the appended claims, the terms“including” and “in which” are used as the plain-English equivalents ofthe respective terms “comprising” and “wherein.” Also, in the followingclaims, the terms “including” and “comprising” are open-ended, that is,a system, device, article, or process that includes elements in additionto those listed after such a term in a claim are still deemed to fallwithin the scope of that claim. Moreover, in the following claims, theterms “first,” “second,” and “third,” etc. are used merely as labels,and are not intended to impose numerical requirements on their objects.

For the purposes of this specification, the term “machine-readablemedium” or “computer-readable medium” shall be taken to include anymedium which is capable of storing or encoding a sequence ofinstructions for execution by the machine and that cause the machine toperform any one of the methodologies of the inventive subject matter.The terms “machine-readable medium” or “computer-readable medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, optical and magnetic disks, and other temporary, transient, orpermanent storage means, such an executable streaming downloadableprogram. Further, it will be appreciated that the software could bedistributed across multiple machines or storage media, which may includethe machine-readable medium.

Method embodiments described herein may be computer-implemented. Someembodiments may include computer-readable media encoded with a computerprogram (e.g., software), which includes instructions operable to causean electronic device to perform methods of various embodiments. Asoftware implementation (or computer-implemented method) may includemicrocode, assembly language code, or a higher-level language code,which further may include computer readable instructions for performingvarious methods. The code may form portions of computer programproducts. Further, the code may be tangibly stored on one or morevolatile or non-volatile computer-readable media during execution or atother times. These computer-readable media may include, but are notlimited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

The Abstract is provided to comply with 37 C.F.R. §1.72(b), whichrequires that it allow the reader to quickly ascertain the nature of thetechnical disclosure. It is submitted with the understanding that itwill not be used to interpret or limit the scope or meaning of theclaims. Also, in the above Detailed Description, various features may begrouped together to streamline the disclosure. This should not beinterpreted as intending that an unclaimed disclosed feature isessential to any claim. Rather, inventive subject matter may lie in lessthan all features of a particular disclosed embodiment. Thus, thefollowing claims are hereby incorporated into the Detailed Description,with each claim standing on its own as a separate embodiment.

1. A method comprising: detecting an alert status of one or moredetectors, the alert status being indicative of a first event occurringin a first timeframe; calculating an alert score at by combining thealert status of the one or more detectors; calculating a composite alertscore, the composite alert score being indicative of a likelihood of anonset of a physiological condition and comprising a combination of twoor more alert scores; comparing the composite alert score to a compositealert score threshold; and providing an indication of the likelihood ofthe onset of the physiological condition when the composite alert scoreviolates the composite alert score threshold.
 2. The method of claim 1,wherein detecting the alert status of the one or more detectorsincludes: detecting a parameter of one or more sensors coupled to theone or more detectors; comparing the parameter to one or more thresholdvalues to obtain a result; and using the result to detect the alertstatus.
 3. The method of claim 2, wherein the parameter includes one ofsensed physiological data, sensed environmental data, or a datacollected from a patient in response to a query or request.
 4. Themethod of claim 3, wherein the sensed physiological data includescardiac indications, physical activity, or indications of patientcompliance.
 5. The method of claim 2, wherein the one or more sensorsinclude an implantable medical device.
 6. The method of claim 2,comprising: choosing an initial value for the one or more thresholdvalues; and dynamically adjusting the one or more threshold values toimprove one or more performance measures related to false positives orfalse negatives for a patient.
 7. The method of claim 6, whereinchoosing the initial value includes using a value determined during alearning period.
 8. The method of claim 1, wherein the alert status isindicative of a heart failure decompensation condition.
 9. The method ofclaim 1, wherein the composite alert score is indicative of a likelihoodof a heart failure decompensation condition, a likelihood of death, or alikelihood of a change in quality of life, in a second timeframe. 10.The method of claim 1, wherein the likelihood of the onset of thephysiological condition indicates a likelihood of the physiologicalcondition presently occurring.
 11. The method of claim 1, wherein thelikelihood of the onset of the physiological condition indicates alikelihood of the physiological condition occurring in the future.
 12. Asystem comprising: a patient device comprising: a communication moduleadapted to detect an alert status of one or more detectors, the alertstatus being indicative of a first event occurring in a first timeframe;and an analysis module adapted to: calculate an alert score by combiningthe detected alerts; calculate a composite alert score, the compositealert score being indicative of a likelihood an onset of a physiologicalcondition and comprising a combination of two or more alert scores; andcompare the composite alert score to a composite alert score thresholdto obtain a result; wherein the communication module is adapted toprovide an indication of the likelihood of the onset of a physiologicalcondition when the result indicates that the composite alert scoreviolates the composite alert score threshold.
 13. The system of claim12, comprising: one or more sensors coupled to the one or moredetectors; wherein the one or more detectors are adapted to: detect aparameter from the one or more sensors; compare the parameter to one ormore threshold values to obtain a result; and use the result to obtainthe alert status.
 14. The system of claim 13, wherein the one or moresensors include an electrocardiogram, an accelerometer, a pressuresensor, a cardiac output detector, a heart rate monitor, aninterrogatory device, a weight scale, or a microphone.
 15. The system ofclaim 13, wherein the one or more sensors include an implantable medicaldevice.
 16. The system of claim 13, wherein the parameter includes oneof sensed physiological data, sensed environmental data, or a datacollected from a patient in response to a query or request.
 17. Thesystem of claim 13, wherein the one or more detectors are adapted to:choose an initial value for the one or more threshold values; anddynamically adjust the one or more threshold values to improve one ormore performance measures related to false positives or false negativesfor a patient.
 18. The system of claim 17, wherein the one or moredetectors are adapted to choose the initial value using a valuedetermined during a learning period.
 19. A machine-readable mediumincluding instructions, which when executed on a machine, cause themachine to: detect an alert status of one or more detectors, the alertstatus being indicative of a first event occurring in a first timeframe;calculate an alert score at by combining the alert status of the one ormore detectors; calculate a composite alert score, the composite alertscore being indicative of a likelihood of an onset of a physiologicalcondition and comprising a combination of two or more alert scores;compare the composite alert score to a composite alert score threshold;and provide an indication of the likelihood of the onset of thephysiological condition when the composite alert score violates thecomposite alert score threshold.
 20. An apparatus comprising: means fordetecting an alert status of one or more detectors, the alert statusbeing indicative of a first event occurring in a first timeframe; meansfor calculating an alert score at by combining the alert status of theone or more detectors; means for calculating a composite alert score,the composite alert score being indicative of a likelihood of an onsetof a physiological condition and comprising a combination of two or morealert scores; means for comparing the composite alert score to acomposite alert score threshold; and means for providing an indicationof the likelihood of the onset of the physiological condition when thecomposite alert score violates the composite alert score threshold.